hardMultiple Choice
PDE Practice Question: A financial services company uses Vertex AI to…
A financial services company uses Vertex AI to serve a fraud detection model. The model was trained on historical data that is updated daily. The team wants to automate retraining when data drift is detected. Which approach best operationalizes this requirement with minimal manual intervention?
⚠ Common exam trap
Google Cloud often tests the distinction between scheduled retraining (Option C) and event-driven retraining triggered by actual drift detection (Option D), where candidates mistakenly choose the simpler scheduled approach without recognizing that it ignores the requirement to retrain only when drift is detected.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable Vertex AI Model Monitoring for feature drift and skew, then create a Cloud Function that triggers a Vertex AI Pipeline to retrain and deploy the model after validation.
It uses Vertex AI Model Monitoring to automatically detect feature drift or skew, then triggers a Cloud Function that invokes a Vertex AI Pipeline to retrain and redeploy the model after validation. This approach minimizes manual intervention by automating both the detection of data drift and the subsequent retraining and deployment lifecycle.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Monitoring alerts on prediction latency to trigger a retraining pipeline.
Why it's wrong here
Prediction latency measures serving responsiveness, not drift in input or feature distributions, so it cannot detect the data drift described. It tempts because latency alerts are simple to wire into pipelines, and they would suit retraining triggered by performance degradation rather than by drift itself.
- ✗
Manually monitor model performance metrics in Vertex AI Experiments and retrain when accuracy drops.
Why it's wrong here
Manual monitoring of Experiments metrics requires human review and intervention, contradicting the minimal-manual-intervention requirement. It tempts because Experiments genuinely tracks accuracy across runs, and manual retraining would be acceptable where drift checks are infrequent or regulatory oversight demands a person approving each retrain.
- ✗
Use scheduled Vertex AI Pipelines to retrain the model every night, then deploy automatically.
Why it's wrong here
A fixed nightly schedule retrains regardless of whether drift occurred, so it does not operationalise drift detection and wastes compute on unchanged data. It tempts because scheduled pipelines are straightforward and suit stable domains where retraining cadence, not drift signal, drives model refresh.
- ✓
Enable Vertex AI Model Monitoring for feature drift and skew, then create a Cloud Function that triggers a Vertex AI Pipeline to retrain and deploy the model after validation.
Why this is correct
Vertex AI Model Monitoring detects feature drift and skew, and a Cloud Function responds by triggering a Vertex AI Pipeline that retrains, validates, and redeploys the model. This closes the loop automatically, satisfying the minimal-manual-intervention constraint for daily-updated fraud data.
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